Learn collision-free self-driving skills at urban intersections with model-based reinforcement learning
Intersection is one of the most complex and accident-prone urban traffic scenarios for
autonomous driving wherein making safe and computationally efficient decisions with high …
autonomous driving wherein making safe and computationally efficient decisions with high …
A Decision-making Approach for Complex Unsignalized Intersection by Deep Reinforcement Learning
S Li, K Peng, F Hui, Z Li, C Wei… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Decision-making for automatic vehicles at unsignalized intersections with dense traffic is
one of the most challenging tasks. Due to the complex structure and frequent traffic …
one of the most challenging tasks. Due to the complex structure and frequent traffic …
A reinforcement learning benchmark for autonomous driving in intersection scenarios
In recent years, control under urban intersection scenarios has become an emerging
research topic. In such scenarios, the autonomous vehicle confronts complicated situations …
research topic. In such scenarios, the autonomous vehicle confronts complicated situations …
Learning to drive at unsignalized intersections using attention-based deep reinforcement learning
H Seong, C Jung, S Lee… - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
Driving at an unsignalized intersection is a complex traffic scenario that requires both traffic
safety and efficiency. At the unsignalized intersection, the driving policy does not simply …
safety and efficiency. At the unsignalized intersection, the driving policy does not simply …
Risk-aware high-level decisions for automated driving at occluded intersections with reinforcement learning
Reinforcement learning is nowadays a popular framework for solving different decision
making problems in automated driving. However, there are still some remaining crucial …
making problems in automated driving. However, there are still some remaining crucial …
State dropout-based curriculum reinforcement learning for self-driving at unsignalized intersections
Traversing intersections is a challenging problem for autonomous vehicles, especially when
the intersections do not have traffic control. Recently deep reinforcement learning has …
the intersections do not have traffic control. Recently deep reinforcement learning has …
Deep Reinforcement Learning-Based Driving Policy at Intersections Utilizing Lane Graph Networks
Learning an efficient and safe driving strategy in a traffic-heavy intersection scenario and
generalizing it to different intersections remains a challenging task for autonomous driving …
generalizing it to different intersections remains a challenging task for autonomous driving …
Self-awareness safety of deep reinforcement learning in road traffic junction driving
Z Cao, J Yun - arXiv preprint arXiv:2201.08116, 2022 - arxiv.org
Autonomous driving has been at the forefront of public interest, and a pivotal debate to
widespread concerns is safety in the transportation system. Deep reinforcement learning …
widespread concerns is safety in the transportation system. Deep reinforcement learning …
Two-stage safe reinforcement learning for high-speed autonomous racing
Decision making for autonomous driving is a safety-critical control problem. Prior works of
safe reinforcement learning either tackle the problem with reward shaping or with modifying …
safe reinforcement learning either tackle the problem with reward shaping or with modifying …
End-to-end intersection handling using multi-agent deep reinforcement learning
AP Capasso, P Maramotti, A Dell'Eva… - 2021 IEEE Intelligent …, 2021 - ieeexplore.ieee.org
Navigating through intersections is one of the main challenging tasks for an autonomous
vehicle. However, for the majority of intersections regulated by traffic lights, the problem …
vehicle. However, for the majority of intersections regulated by traffic lights, the problem …
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